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Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

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Showing 13151–13200 of 17610 papers

TitleStatusHype
Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training—0
Handwriting recognition and automatic scoring for descriptive answers in Japanese language tests—0
Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning—0
An Ensemble Approach to Acronym Extraction using TransformersCode0
Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language frameworkCode0
Low-Rank Constraints for Fast Inference in Structured ModelsCode0
Imagined versus Remembered Stories: Quantifying Differences in Narrative Flow—0
Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition—0
A Transfer Learning Pipeline for Educational Resource Discovery with Application in Leading Paragraph Generation—0
Formal Analysis of Art: Proxy Learning of Visual Concepts from Style Through Language Models—0
Interactive Attention AI to translate low light photos to captions for night scene understanding in women safety—0
Submix: Practical Private Prediction for Large-Scale Language Models—0
Technology Mapping Using WebAI: The Case of 3D Printing—0
Rethinking Controllable Variational Autoencoders—0
Training and Generating Neural Networks in Compressed Weight SpaceCode0
TextRGNN: Residual Graph Neural Networks for Text Classification—0
Evaluating Contextual Embeddings and their Extraction Layers for Depression Assessment—0
Secondary Use of Clinical Problem List Entries for Neural Network-Based Disease Code Assignment—0
Event-based clinical findings extraction from radiology reports with pre-trained language modelCode0
CABACE: Injecting Character Sequence Information and Domain Knowledge for Enhanced Acronym and Long-Form ExtractionCode0
Multi-Dialect Arabic Speech Recognition—0
Counterfactual Memorization in Neural Language Models—0
Towards more patient friendly clinical notes through language models and ontologies—0
The Importance of the Current Input in Sequence Modeling—0
Diformer: Directional Transformer for Neural Machine Translation—0
Efficient Large Scale Language Modeling with Mixtures of Experts—0
Spiral Language Modeling—0
Lerna: Transformer Architectures for Configuring Error Correction Tools for Short- and Long-Read Genome Sequencing—0
Integrating Knowledge in End-to-End Automatic Speech Recognition for Mandarin-English Code-Switching—0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey—0
Pretrained Language Models Are All You Need For Text-to-SQL Schema Linking—0
AutoGraphex: Zero-shot Biomedical Definition Generation with Automatic Prompting—0
Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge—0
CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domainCode0
DOCmT5: Document-Level Pretraining of Multilingual Language Models—0
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning—0
Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems—0
Lacuna Reconstruction: Self-supervised Pre-training for Low-Resource Historical Document Transcription—0
Reconsidering the Past: Optimizing Hidden States in Language Models—0
Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language ModelingCode0
Simple Text Detoxification by Identifying a Linear Toxic Subspace in Language Model Embeddings—0
Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation—0
Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort—0
Aspect Oriented Suggestion Extraction from Online Reviews—0
Applying SoftTriple Loss for Supervised Language Model Fine Tuning—0
Epigenomic language models powered by Cerebras—0
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model CompressionCode0
CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising—0
Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model—0
Few-shot Multi-hop Question Answering over Knowledge Base—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified